Dynamic parameter control of interactive local search in UML software design

Abstract

User-centered Interactive Evolutionary Computation (IEC) has been applied to a wide variety of areas, including UML software design. The performance of evolutionary search is important as user interaction fatigue remains an on-going challenge in IEC. However, to obtain optimal search performance, it is usually necessary to “tune” evolutionary control parameters manually, although tuning control parameters can be time-consuming and error-prone. To address this issue in other fields of evolutionary computation, dynamic parameter control including deterministic, adaptive and self-adaptive mechanisms have been applied extensively to real valued representations. This paper postulates that dynamic parameter control may be highly beneficial to IEC in general, and UML software design in particular, wherein a novel objectbased solution representation is used. Three software design problems from differing design domains and of differing scale have been investigated with mutation probabilities modified by simulated annealing, the Rechenberg “1/5 success rule” and self-adaptation within local search. Results indicate that selfadaptation appears to be the most robust and scalable mutation probability modification mechanism. The use of self-adaption with an object-based representation is novel, and results indicate that dynamic parameter control offers great potential within IEC.

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